3D-Printed Magnetic Levitation Device with Deep Learning-Assisted Particle Tracking and Analysis for High-Throughput Sorting

Abstract

Intrinsic density-based particle separation is fundamental to biomedical research and materials science. Magnetic levitation offers an accessible and label-free approach; however, current platforms are limited by throughput, complex fabrication requirements, and manual analysis methods. Here, we demonstrate a high-throughput magnetic levitation-based microfluidic device fabricated using commercial 3D printing, integrated with dual automated analysis systems. The device features optimized magnet configurations and wide channel design (1 mm x 1 mm) that enables gentle separation (<1 PSI) at throughputs of 66 mL/hr-- a ten-fold improvement over existing levitation platforms. We developed two complementary analysis tools: "Phase" for static levitation height measurements, and a deep learning pipeline combining CNN-based particle classification (>95% accuracy) with SORT (Simple Online and Realtime Tracking) algorithm for dynamic analysis. The automated system showed excellent correlation with manual counting (Pearson coefficients: 0.91-0.99, p<0.001). Through systematic optimization of magnet spacing and paramagnetic medium concentration (150 mM Gd), the platform achieved robust continuous-flow sorting while maintaining exceptional purity (>90%) and resolving density differences as small as 0.03 g/mL. This work establishes a versatile platform for particle sorting, enabling sophisticated analysis without specialized facilities or extensive operator training, with broad applications in biomedical research and diagnostics.